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Non-linear Quantum State Design for Adaptive Quantum Circuit Engineering

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to quantum circuit design leveraging non-linear quantum states. Traditional quantum circuit design relies on pre-defined gate sequences and static parameter adjustments. This work introduces a self-adaptive circuit design method that dynamically adjusts the circuit's structure and parameters based on the inherent dynamics of the quantum state. We propose a machine learning-driven system to optimize the circuit's performance by continuously refining the circuit's configuration. The core mechanism centers around the quantification of quantum state evolution and its subsequent application to circuit design. The design offers a significant advancement over existing techniques, providing a more flexible and potentially high-performing solution for complex quantum circuit architectures.

DOI
10.5281/zenodo.22467342

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Quantum circuit design presents significant challenges due to the inherent fragility of quantum states and the complexity of controlling their evolution. Traditional methods often rely on static, pre-defined topologies, limiting the potential for optimization and adaptation to specific quantum systems. This paper introduces a novel approach to quantum circuit design, leveraging adaptive topology – a concept where the circuit's topology dynamically adjusts based on the quantum system's characteristics – to enhance circuit performance and stability. We propose a method for automatically generating and optimizing topological circuits using a reinforcement learning framework. The core mechanism involves a self-adaptive topological graph construction, iteratively refining the circuit's structure to minimize errors and maximize fidelity. The study demonstrates the effectiveness of this approach through simulations and experimental validation on a simplified quantum system, showing significant improvements in performance compared to conventional methods. The paper concludes with a discussion of the potential applications and future research directions in this rapidly evolving field.
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This paper presents a novel approach to quantum circuit design utilizing a non-symmetric matrix representation. Traditional circuit design relies heavily on manual parameter tuning, often limiting design flexibility and potentially impacting performance. We propose a self-adaptive circuit design system, driven by a dynamically adjusted matrix representation of the quantum system. The system leverages quantum entanglement, specifically the concept of "quantum pulses," to automatically adjust parameters, optimizing circuit performance based on input data. The core mechanism involves a matrix transformation that governs the quantum circuit's evolution, allowing the circuit to adapt to varying data characteristics. This design offers a significant advancement over existing methods, providing a mechanism for automatic circuit optimization and improved performance, particularly in scenarios with complex data. This work explores the mathematical foundations of this system and its potential implications for quantum circuit design.